An Efficient Baseball Playfield Segmentation Based on Learning Vector Quantization

The segmentation of playfield is essential because it can offer higher level content analysis for sport videos. In this paper, a simple but efficient classification scheme is introduced which is able to adapt to the variations of field colors in diverse baseball videos. First, we utilize learning vector quantization (LVQ) to classify the grass and soil colors of playfields in YUV color space, and then propose the filed map feature that possesses class concept rather than low-level feature and it can also preserve the layout of playfield. Experimental results using three different popular baseball video types revealed that the proposed method is robust and can recognize grass soil and other samples accurately.